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Figure 3: Image was generated using 3-D Slicer (V4.3) [57]. The stability map denotes that the darker ROIs tend to be more affected by AD, hence more sensitive to the AD progression than the brighter regions. The features extracted from the darker areas are also considered as more stable predictors for our deep learning model and tend to be beneficial to all of the hidden neurons in the neural network.

Figure 4: Visualisation of the current robot configuration, end-effector position and the 3D Model from a sawbone specimen in 3D Slicer using the incoming data from the robot control and the experimental setup at the Institute of Mechatronic Systems

Figure 2: The comparison between the variations of low-dimensional biomarkers and neuroimaging data. (a) is the plot of MMSE scores. (b) are slices and MAPER whole
brain mask models from ADNI baseline cohort, generated using 3D Slicer 4.3.1 [19]. The neuroimaging data contains more information than MMSE. Some sophisticated features, such as ventricle sizes and atrophy, cannot be captured by MMSE, but can be shown in neuroimaging data.

Figure 1: Illustrative example of discordant localization of the suspected prostate cancer (PCa), where whole mount pathology (WMP) was necessary to accurately identify the PCa. Top: PCa localization using whole mount annotations as the reference; the PCa chosen was identified on the left of the patient's prostate, as defined from WMP. Bottom: PCa localization using SPR, however, chose a suspicious-appearing lesion on the right as the PCa (outlined in green). Note: normal peripheral zone is outlined in yellow on the same slide in this case. ADC, apparent diffusion coefficient; SPR, surgical pathology report; T2W, T2-weighted imaging; WMP, whole mount pathology.

Figure 1: Regions of interest (ROI) placement on PET and corresponding magnetic resonance images. The figure shows an example of the ROI placement in gray and white matter regions and in germinal zones on magnetic resonance imaging (upper panel) and coregistration with corresponding PET scans (lower panel). From a total of 24 regions both SUVmean and SUVmax were obtained. SUVs were analyzed longitudinally over time for each patient in all ROI, to include a pre- and postchemotherapy data set. (1,2): right and left cerebellar cortex; (3,4): right and left
medial temporal lobe and hippocampus; (5,6): right and left orbitofrontal cortex/olfactory gyrus; (7,8): right and left anterior and posterior subventricular zone; (9,10): right and left caudate nucleus; (11,12): right and left thalamus; (13,14): right and left periventricular white matter;
(15): corpus callosum (splenium); (16,17): right and left frontal cortex; (18,19): right and left subcortical white matter; Not shown in this representative image are: right and left parietal cortex, corpus callosum (genu), posterior cingulate, and cerebellar white matter. PET, positron
emission tomography; SUV, standardized uptake value.

Figure 2: Three-dimensional model of TBI anatomy for Patient 1 (acute baseline time point), as generated in 3D Slicer. Non-hemorrhagic and hemorrhagic lesions are displayed in cyan and red, respectively. The GM is shown using a transparent model.

Figure 1: Schematic diagram depicting the overview of the analysis. A: First, we performed five manual delineations and six 3D-Slicer segmentations (three observers twice) on twenty lung tumors. B: Second, fifty-six radiomic features quantifying tumor intensity, texture and shape were extracted from these segmentations. C: Third, the resulting feature matrices were compared for robustness of the feature values.